Algorithm GUESS is almost identical to a probabilistic search
algorithm used in previous work on applied inductive inference
[#!Schmidhuber:95kol!#,#!Schmidhuber:97nn!#]. The
programs generated by the previous algorithm, however,
were not bitstrings but written in an assembler-like language;
their runtimes had an upper bound, and the
program outputs were evaluated as to whether they represented solutions
to externally given tasks.

Using a small set of exemplary training examples, the system
discovered the weight matrix of an artificial neural network whose task
was to map input data to appropriate target classifications. The network's
generalization capability was then
tested on a much larger unseen test set. On several toy problems it generalized
extremely well in a way unmatchable by traditional neural network
learning algorithms.

The previous papers, however, did not explicitly establish
the above-mentioned relation between ``optimal'' resource
bias and GUESS.